A big data approach to analyzing market volatility

Algorithmic Finance

Volumn 2, Issue 3-4, 2013, Pages 241-267

  Wu, Kesheng ^a   Bethel, E Wes ^a   Gu, Ming ^b   Leinweber, David ^a   Rübel, Oliver ^a  

^a LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84924532479     PISSN: 21585571     EISSN: 21576203     Source Type: Journal    
DOI: 10.3233/AF-13030     Document Type: Article

References (40)

1. Abad, D., Yagöe, J., 2012. From PIN to VPIN: An introduction to order flow toxicity. Spanish Rev. Finan. Econ. 10(2), 74-83.
2. Admati, A.R., Pfleiderer, P., 1988. A theory of intraday patterns: Volume and price variability. Rev. Finan. Stud. 1(1), 3-40.
3. Aït-Sahalia, Y., Mykland, P.A., Zhang, L., 2005. How often to sample a continuous-time process in the presence of market microstructure noise. Rev. Finan. Stud. 18(2), 351-416.
4. Amihud, Y., Mendelson, H., Murgia, M., 1990. Stock market microstructure and return volatility: Evidence from Italy. J. Bank. Finan. 14(2), 423-440.
5. Andersen, T.G., Bollerslev, T., 1998. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. Int. Econ. Rev. 39, 885-905.
6. Andersen, T.G., Bollerslev, T., Diebold, F.X., & Labys, P., 2001. The distribution of realized exchange rate volatility. J. Am. Stat. Assoc. 96(453), 42-55.
7. Bethel, E.W., Leinweber, D., Röbel, O., Wu, K., 2012. Federal market information technology in the post-flash crash era: Roles for supercomputing. J. Trad. 7(2), 9-25. http://dx.doi.org/10.3905/jot.2012.7.2.009.
8. Bethel, E.W., Childs, H., 2012. High Performance Visualization: Enabling Extreme-scale Scientific Insight. Boca Raton, FL: Chapman & Hall.
9. Blume, L., Easley, D., Ohara, M., 1994. Market statistics and technical analysis: The role of volume. J. Finan. 49(1), 153-181.
10. Chakrabarty, B., Pascual, R., Shkilko, A., 2012. Trade classification algorithms: A horse race between the bulk-based and the tick-based rules. http://ssrn.com/abstract=2182819.
11. Colby, R.W., Meyers, T.A., 1988. The Encyclopedia of Technical Market Indicators. USA: Dow Jones- Irwin.
12. Crow, E.L., Shimizu, K., 1988. Lognormal distributions: Theory and applications. Vol. 88. New York, NY: CRC PressI Llc.
13. de Prado, M., 2012. Advances in High Frequency Strategies, PhD thesis, Cornell University.
14. Easley, D., de Prado, M.L., OHara, M., 2011a. The exchange of flow toxicity. J. Trad. 6(2), 8-13.
15. Easley, D., de Prado, M.L., OHara, M., 2011b. The microstructure of the flash crash: Flow toxicity, liquidity crashes and the probability of informed trading. J. Portfolio Manage. 37, 118-128.
16. Easley, D., de Prado, M.L., OHara, M., 2012a. Flow toxicity and liquidity in a high frequency world. Rev. Finan. Stud. 25(5), 1457-1493.
17. Easley, D., Kiefer, N., OHara, M., Paperman, J., 1996. Liquidity, information, and infrequently traded stocks. J. Finan. 51, 1405-1436.
18. Easley, D., de Prado, M.L., OHara, M., 2012b. The volume clock: Insights into the high frequency paradigm. Available at SSRN http://ssrn.com/abstract=2034858.
19. Easley, D., OHara, M., 1992. Time and the process of security price adjustment. J. Finan. 47(2), 577-605.
20. Gamma, E., Helm, R., Johnson R., Vlissides, J., 1995. Design Patterns. Addison-Wesley, Reading, Massachusetts.
21. Gropp, W., Lusk, E.W., Skjellum, A., 1999. Using MPI: Portable Parallel Programming with the Message Passing Interface, Vol. 1. Cambridge, MA: MIT press.
22. Hager, G., Wellein, G., 2010. Introduction to high performance computing for scientists and engineers. Boca Raton, FL: CRC Press.
23. HDF Group., 2011. HDF5 user guide. http://hdf.ncsa .uiuc.edu/HDF5/doc/H5.user.html.
24. Hey, T., Tansley, S., Tolle, k., eds ., 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Redmond, WA: Microsoft.
25. Kirilenko, AA., Kyle, A.P.S., Samadi, M., Tuzun, T., 2010. The flash crash: The impact of high frequency trading on an electronic market. Available at SSRN: http://ssrn.com/abstract=1686004.
26. Kumar, V., Grama, A., Gupta, A., Karypis, G., 1994. Introduction to Parallel Computing. Benjamin/Cummings, Redwood City.
27. Lee, C., Radhakrishna, B., 2000. Inferring investor behavior: Evidence from torq data. J. Finan. Markets 3(2), 83-111.
28. Lee, C., Ready, M.J., 1991. Inferring trade direction from intraday data. J. Finan. 46, 733-746.
29. Lewis, B., Berg, D.J., et al. 1998. Multithreaded Programming with Pthreads. Sun Microsystems Press.
30. Mattson, T.G., Sanders, B.A., Massingill, B.L., 2004. Patterns for Parallel Programming. Indianapolis, IN: Addison-Wesley Professional.
31. Menkveld, A.J., Yueshen, B., 2013. Anatomy of the flash crash. Available at SSRN http://ssrn.com/abstract=2243520.
32. OHara, M., 2007. Market Microstructure Theory. Cambridge, UK: Blackwell.
33. Shell, D.L., 1959. A high-speed sorting procedure. Commun. ACM. 2(7), 30-32.
34. Shiller, R.J., 1992. Market Volatility. Cambridge, MA: The MIT Press.
35. Shoshani, A., Rotem, D., 2010. Scientific Data Management: Challenges, Technology, and Deployment, Vol. 3. Boca Raton, FL: Chapman and Hall/CRC.
36. Stroustrup, B., 1994. C++ Programming Language, 3/e. Reading, MA: Pearson Education India.
37. Theissen, E., 2001. A test of the accuracy of the lee/ready trade classification algorithm. J. Int. Finan. Markets Inst. Money 11(2), 147-165.
38. Van Rossum, G., et al. 1994. Python programming language. http://python.org/.
39. Vives, X., 2010. Information and Learning in Markets: the Impact of Market Microstructure. Princeton, NJ: Princeton University Press.
40. White, T., 2012. Hadoop: The Definitive Guide. Sebastopol, CA: OReilly Media, Inc.